BCIwiki (bciwiki.com) — Researchers have released iMINDBench, a multi-institution benchmark for intracranial EEG (iEEG) neural decoding that puts three naturalistic movie-watching datasets, 15 decoding tasks, standardized preprocessing tracks and fixed evaluation splits into one evaluation framework. The preprint was posted to arXiv on September 16, 2026, and has not been peer reviewed.
Intracranial EEG records electrical activity directly from electrodes inside the human brain, making it an attractive modality for neural decoding. But the team notes that progress in the field is hard to measure reliably: datasets are task- or institution-specific, limiting evidence of generalization across tasks and recording environments, and preprocessing choices strongly influence performance, making model improvements difficult to distinguish from preprocessing gains.
Evaluated on iMINDBench, the pretrained systems tested generally outperformed baselines within their respective preprocessing tracks, while strong spectral baselines remained competitive across institutional datasets.
A scaling study produced a blunter result: adding up to 25 times more supervised data from other subjects or institutions yielded only small or task-dependent gains over within-session training. The team concludes that iEEG models need to improve on strong preprocessing baselines and make more effective use of data across subjects and institutions.
The preprint runs 26 pages with 13 figures. Its authors are Geeling Chau, Saba Hashemi, Yonghyeon Gwon, Eshani Patel, Jan DeWitt, Christopher Wang, Andrii Zahorodnii, Sabera J Talukder, Danny Dongyeop Han, Chun Kee Chung, Maryam M Shanechi and Yisong Yue.